[Note: DataFramed is currently on a short hiatus, but we plan to bring it back during Summer, 2019.] Data science is one of the fastest growing industries and has been called the ‘Sexiest job of the 21st Century’. But what exactly is data science? In this podcast, brought to you by DataCamp, Hugo Bowne-Anderson approaches the question by exploring what problems data science can solve rather than defining what data science is. From automated medical diagnosis and self-driving cars to recommendation systems and climate change, come on a journey with experts from industry and academia to explore the industry that will change the course of the 21st century.
Cathy and Hugo discuss the current lack of fairness in artificial intelligence, how societal biases are perpetuated by algorithms and how both transparency AND audit-ability of algorithms will be necessary for a fairer future.
Arnaub Chatterjee, a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company, discusses cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare.
How data science works at Stitch Fix, an online personal styling service reinventing the shopping experience by delivering one-to-one personalization to their clients through the combination of data science and human judgment.
What does it means to be a data consultant, what industries does Tanya works in and what is the impact of data products in her work and the importance of rapid prototyping and getting MVPs or minimum viable products out the door?
What is the role of data science in product development at github, what does it means to “use computation to build products to solve real-life decision making, practical challenges” and what does building data products at github actually looks like?
What are best practices for organizing data science teams? Having data scientists distributed through companies or having a Centre of Excellence? What are the most important skills for data scientists? Is the ability to use the most sophisticated deep learning models more important than being able to make good powerpoint slides?
How does data science help Buzzfeed achieve online virality? What type of mass online experiments do data scientists at BuzzFeed run for this purpose? What products do they develop to make all of this easy and intuitive for content producers?